archimedes.odeint¶

archimedes.odeint(

func: Callable,

t_span: tuple[float, float],

x0: ArrayLike,

method: str = 'cvodes',

t_eval: NDArray | None = None,

atol: float = 1e-6,

rtol: float = 1e-3,

args: Sequence[Any] | None = None,

static_argnames: str | Sequence[str] = (),

**options,

) → ArrayLike¶

Integrate a system of ordinary differential equations.

Solves the initial value problem defined by the ODE system:

\[\dot{x} = f(t, x, \theta)\]

from t=t_span[0] to t=t_span[1] with initial conditions x0.

Parameters:

func (callable) – Right-hand side of the ODE system. The calling signature is func(t, x, *args), where t is a scalar and x is an ndarray with shape (n,). func must return an array with shape (n,).
t_span (tuple of float) – Interval of integration (t0, tf).
x0 (array_like) – Initial state.
method (str, optional) – Integration method to use. Default is "cvodes", which uses the SUNDIALS CVODES solver (suitable for both stiff and non-stiff systems).
t_eval (array_like or None, optional) – Times at which to store the computed solution. If None, the solver only returns the final state.
atol (float, optional) – Absolute tolerance for the solver. Default is 1e-6.
rtol (float, optional) – Relative tolerance for the solver. Default is 1e-3.
args (tuple, optional) – Additional arguments to pass to the ODE function.
static_argnames (tuple of str, optional) – Names of arguments to func that should be treated as static (not traced symbolically).
**options (dict) – Additional options to pass to the CasADi integrator.

Returns:

If t_eval is None, the state at the final time t_span[1]. If t_eval is provided, an array containing the solution evaluated at t_eval with shape (n, len(t_eval)).

Return type:

array_like

Notes

The underlying SUNDIALS solvers use adaptive step size control to balance accuracy and computational efficiency. However, unlike SciPy’s solve_ivp function and many other ODE solver interfaces, it is not possible to output the solution at the times actually used by the solver (or to create a “dense” output using an interpolant). This is because of the requirement that all input and output arrays be a fixed size in order to construct the symbolic graph. The solution at output times specified by t_eval is computed by interpolating the solution at the times used by the solver.

The ODE solver is specified by the method argument. The default is "cvodes", which uses the SUNDIALS CVODES solver and is suitable for both stiff and non-stiff systems. This is the recommended choice for most applications.

See the CasADi documentation for a complete list of available configuration options to pass to the options argument.

Conceptual model: This function is a wrapper around the :py:func:integrator function that creates and immediately calls an ODE solver for the given initial conditions and parameters. It provides a simple interface similar to SciPy’s solve_ivp but leverages CasADi’s efficient symbolic implementation and interface to SUNDIALS solvers.

Unlike integrator(), which creates a reusable solver function, odeint performs a single solve operation. If you need to solve the same ODE system repeatedly with different initial conditions or parameters, it’s more efficient to create an integrator function once and reuse it.

Limitations:

Only scalar and vector states are supported (shapes like (n,) or (n,1))
For tree-structured states, currently the user must define a wrapper function to flatten and reconstruct the tree structure

Examples

>>> import numpy as np
>>> import archimedes as arc
>>> import matplotlib.pyplot as plt
>>>
>>> # Example 1: Simple harmonic oscillator
>>> def f(t, y):
>>>     return np.array([y[1], -y[0]], like=y)  # y'' = -y
>>>
>>> t_span = (0, 10)
>>> y0 = np.array([1.0, 0.0])  # Initial displacement and velocity
>>> ts = np.linspace(*t_span, 100)
>>>
>>> # Solve ODE
>>> ys = arc.odeint(f, t_span, y0, t_eval=ts)
>>>
>>> plt.figure()
>>> plt.plot(ts, ys[0, :], label='position')
>>> plt.plot(ts, ys[1, :], label='velocity')
>>> plt.legend()
>>> plt.show()
>>>
>>> # Example 2: Parametrized dynamics
>>> def pendulum(t, y, g, L):
>>>     theta, omega = y
>>>     return np.array([omega, -(g/L) * np.sin(theta)])
>>>
>>> y0 = np.array([np.pi/6, 0.0])  # 30 degrees initial angle
>>> t_span = (0, 5)
>>> ys = arc.odeint(pendulum, t_span, y0, args=(9.81, 1.0))